Cyber-Physical Production Networks: Artificial Intelligence Data-driven Internet of Things Systems, Smart Manufacturing Technologies, and Real-Time Process Monitoring.

AuthorHodgkins, Stacy
  1. Introduction

    With the swift expansion of harmonizing data and operation technologies in the production sector, outstanding endeavors will make manufacturing smart. (Lu et al., 2020) Enterprise data systems configured in networked, interoperable, adjustable, and reactive supervision frameworks enable the coherent harnessing of data available to handle Industry 4.0-oriented operations. (Derigent et al., 2020) Data analytics can diminish process variation and cut down product failures in manufacturing time series. (Sun et al., 2020)

  2. Conceptual Framework and Literature Review

    Relevant alterations are pivotal in the shift from non-automated to a smart factory system, particularly with respect to the organizational approach (Byrne, 2019; Kovacova et al, 2019; Hollowell et al, 2019; Popescu et al, 2018), the configuration of system and its operations, and the integration of Industry 4.0 technologies at all production stages. (Jerman et al., 2020) Assimilating cutting-edge technologies with big data analytics platforms in the framework of digital shift (Du?manescu et al, 2016; Kovacova and Kliestik, 2017; Harrower, 2019; Popescu, 2014; Valaskova et al., 2018) may be instrumental in advancing the manufacturing sector and enhancing its performance. (de la Fuente-Mella et al., 2020) Smart, networked devices, which are reinforced by cutting-edge information and communication technology (Durst, 2019; Petcu, 2018; Lazaroiu et al, 2017; Swadzba, 2019), may ground-breakingly interconnect, and gather, process, and generate data. (Huo et al, 2020)

  3. Methodology and Empirical Analysis

    Building my argument by drawing on data collected from AMG World, Deloitte, Forrester, PwC, SME, and teknowlogy, I performed analyses and made estimates regarding artificial intelligence application and deployment (by industry, %), top three goals that would drive companies to invest in new manufacturing technologies (%), and how industrial companies are getting closer to customers (%). The structural equation modeling technique was used to test the research model.

  4. Results and Discussion

    A broad variety of software and hardware building blocks operate in manufacturing systems and factories harnessing heterogeneous interfaces and input formats for data exchange on several stages of the digital architecture. (Schmetz et al., 2020) Collaborative robots in Industry 4.0 are used for elaborate processes and made-to-order manufacturing activities. (Tannous et al, 2020) Industry 4.0 will have an impact on the integrated value chain and optimize the performance of the production system, regarding networking across factories and beyond. (Garcia et al., 2020) Integrated operations planning is an important condition for coherent implementation of smart manufacturing approaches in various industrial sectors. (Kumar et al, 2020) Growing customized demands in products involves outstanding adjustability in the manufacturing systems, according to constant market changes. (Leng et al, 2020) (Tables 1-9)

  5. Conclusions and Implications

    The setting up of groundbreaking decision support systems for companies in the manufacturing sector and the harnessing of data mining technologies constitute breakthroughs to...

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